Objective Sleep Quality and the Underlying Functional Neural Correlates Among Older Adults with Possible Mild Cognitive Impairment
Bibliographic record
Abstract
BACKGROUND: Poor sleep quality is common among older individuals with mild cognitive impairment (MCI) and may be a consequence of functional alterations in the brain; yet few studies have investigated the underlying neural correlates of actigraphy-measured sleep quality in this cohort. OBJECTIVE: The objective of this study was to examine the relationship between brain networks and sleep quality measured by actigraphy. METHODS: In this cross-sectional analysis, sleep efficiency and sleep fragmentation were estimated using Motionwatch8 (MW8) over a period of 14 days in 36 community-dwelling older adults with possible MCI aged 65-85 years. All 36 participants underwent resting-state functional magnetic resonance imaging (fMRI) scanning. Independent associations between network connectivity and MW8 measures of sleep quality were determined using general linear modeling via FSL. Networks examined included the somatosensory network (SMN), frontoparietal network (FPN), and default mode network (DMN). RESULTS: Across the 36 participants (mean age 71.8 years; SD = 5.2 years), mean Montreal Cognitive Assessment score was 22.5 (SD = 2.7) and Mini-Mental State Examination score was 28.3 (SD = 1.5). Mean sleep efficiency and fragmentation index was 80.1% (SD = 10.0) and 31.8 (SD = 10.4) respectively. Higher sleep fragmentation was significantly correlated with increased connectivity between the SMN and insula, the SMN and posterior cingulate, as well as FPN and primary motor area (FDR-corrected, p < 0.004). CONCLUSION: Functional connectivity between brain regions involved in attentional and somatosensory processes may be associated with disrupted sleep in older adults with MCI.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".